93064 - STATISTICS

Conoscenze e abilità da conseguire

The aim of the course is to introduce the elementary concepts of descriptive statistics, probability, statistical inference, and linear regression. The course will provide students with the basic knowledge to develop applied quantitative analyses of complex social and economic phenomena such as those characterizing the modern tourism sector and the urban economy. Prerequisite is knowledge of basics of Mathematics.

Contenuti

1st half

Introduction to the R language and the software RStudio. Data frames, observations, variables, computing and interpreting means.

Basics of estimating causal effects with randomized controlled trials. Randomized experiment, treatment group, control group. Examples and case studies in R. Difference-in-means estimator.

Inferring population characteristics via survey research. Sample, population, random sampling, frequency table of a variable, table of proportions, histogram of a variable, density histogram, descriptive statistics (mean, median, standard deviation, variance), z-score, correlation between two variables.

Predicting outcomes using linear regression. The linear model. Outcome and predictor variables. Fitted linear model, estimated intercept and estimated slope. Linear regression with binary outcome variables. Connection to the difference-in-means estimator.

Basics of estimating causal effects with observational data. Confounding variables.

 2nd half

Basics of Probability theory. Probability distribution, Bernoulli distribution, Normal distribution, probability density function of the Normal distribution, Standard normal distribution. Sample mean. Law of large numbers and central limit theorem. Sampling distribution of the sample mean.

Quantifying uncertainty. Parameter, estimate, estimator. Sampling distribution of an estimator. Standard error of an estimator. Confidence intervals. Hypothesis testing.

Testi/Bibliografia

Text book:

Data Analysis for Social Science. A friendly and practical introduction. Alena Llaudet, Kosuke Imai. (Princeton University Press).

 

Further readings:

Statistical Methods for the Social Sciences. Alan Agresti (5th edition, Pearson).

The Art of Statistics: How to Learn from Data. David Speigelhalter

Metodi didattici

Frontal lectures with the help of slides, blackboard. You will use your own laptop in class for the practical sessions with Rstudio. The classroom is wired, so bring your own laptop at class.

Considering the nature of the activities and the teaching methods adopted, the attendance of this training activity requires all students to participate in the safety modules 1 and 2 on studying places [https://elearning-sicurezza.unibo.it/ ] in e-learning mode.

Modalità di verifica e valutazione dell'apprendimento

Exam aim

Evaluate whether students:

1) are able to recognize and interpret quantitative information; in particular, read and understand quantitative data in various formats, communicate the meaning of quantitative data and the results of data analysis;

2) understand the theoretical basis of quantitative reasoning. In particular, explain the basic concepts of quantitative reasoning, such as variables, constants, and estimates; understand how inferences are drawn from quantitative analysis and recognize the limitations of quantitative methods;

3) understand the practical application of quantitative data analysis. In particular, whether they are able to determine and use appropriate quantitative methods to solve problems and accurately interpret the results.

 

Grading policy

minimum passing grade: 18/30; maximum passing grade 30/30.

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Option A: the final grade is the sum of:

- Homework (10 points out of 30).

Four homework problems (HW) will be assigned by the teacher every second week (2 HWs before midterm and 2 after midterm). The average grade (averaging over the 4 HWs) will be communicated at the end of the course. Students who get maximum grade at each HW get 2 extra points, i.e. 10+2. More info in class about the policy applied to collaboration between students (which is permitted under some conditions) and late submissions.

- Midterm exam (covers 1st half, 10 points out of 30)

- Final Exam (covers 2nd half, 10 points out of 30).

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Option B: the final grade is the average of Midterm exam (grades from 18/30 to 30/30) and Final exam (grades from 18/30 to 30/30).

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Option C: the final grade is determined by the Total exam (covers both 1st and 2nd half), grades from 18/30 to 30/30.

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Both the midterm, final and total exams are administered via EOL in a University Lab (Unibo credentials are required to log in computers, so please make sure you remember your username and password!). The exam is a computer-based written exam, containing multiple choice questions and questions requiring text and numbers. Students are allowed to use a single letter-sized (A4) sheet of hand-written notes (two-sided).

Students who attend STATISTICS 93064 for the first time (i.e., first year students) can chose any option, but option A is highly recommended! Students choosing either option A or B must complete the exam in the first exam date in summer (i.e. a sufficient grade obtained at midterm will remain valid until the first exam date in summer).

Students who have already attended STATISTICS 93064 once in the past (e.g., second year students) can only choose B or C.

NOTE: the midterm and final exams for people choosing option A will be shorter than for options B, as the students taking option A have already accomplished part of the exam by doing homework.

 

About enrollment, grades and registration, rejecting grades.

1) Students are required to enroll via almaesami website, you can do this until 3 days before the exam date (please make sure you enroll otherwise the exam organization gets complicated and will run in delays).

2) Students will be notified via email of their grades, which are are published on almaesami, and the date set for registration. Registration usually takes place a week after publication.

3) Students having passed the exam successfully can reject the grade once. (To this end, they must email a request to the instructor within the date set for registration. The instructor will confirm reception of the request within the same date. Rejection is intended with respect to the final grade, not midterm grades.)

 

 

Strumenti a supporto della didattica

Slides, blackboard, laptop for practicing Rstudio.

RStudio is based on the R language. Both R and RStudio are free softwares that are installed in lab computers, I recommend students download and install both R and RStudio in their laptops.  We will use RStudio Desktop version. First install R and then RStudio Desktop.

 

To download R

https://cran.r-project.org

To download RStudio Desktop:

https://posit.co/download/rstudio-desktop/

Orario di ricevimento

Consulta il sito web di Massimo Ventrucci